Mapping Socio-Economic Divides with Urban Mobility Data

2025-10-06

AI Summary

This study quantitatively characterizes intra-urban socioeconomic spatial segregation using digital footprints from Shanghai’s shared-bike system. Method: We construct a multi-source heterogeneous dataset integrating LLM-enhanced economic attribute annotation, built-environment features, and spatiotemporal contextual variables; propose a “club effect” to describe resource spatial agglomeration in high-income neighborhoods; identify functional differentiation in trip purposes; reveal an inverted-U-shaped usage pattern dominated by middle-income groups; and employ interpretable random forest modeling. Contribution/Results: Housing price emerges as the strongest predictor of shared-bike usage patterns—significantly outperforming all other spatiotemporal covariates—and enables fine-grained mapping of socioeconomic stratification within the city. This work establishes a novel, interpretable analytical framework for assessing urban equity using mobile big data.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Data transparency and provenance

Abstract

The massive digital footprints generated by bike-sharing systems in megacities like Shanghai offer a novel perspective on the urban socio-economic fabric. This study investigates whether these daily mobility patterns can quantitatively map the city's underlying social stratification. To overcome the persistent challenge of acquiring fine-grained socio-economic data, we constructed a multi-layered analytical dataset. We annotated 2,000 raw bike trips with local economic attributes, derived from a novel data enrichment methodology that employs a Large Language Model (LLM), and integrated contextual features of the built environment. A Random Forest model was then utilized as an interpretable framework to determine the key factors governing the relationship between mobility behavior and local economic status. The analysis reveals a compelling and unambiguous finding: a neighborhood's economic level, proxied by housing prices, is the single most dominant predictor of its bike-sharing patterns, substantially outweighing other geographic or temporal factors. This economic determinism manifests in three distinct ways: (1) a spatial clustering of resources, a phenomenon we term the extit{club effect}, which concentrates mobility infrastructure and usage in affluent areas; (2) a functional dichotomy between necessity-driven, utilitarian usage in lower-income zones and flexible, recreational usage in wealthier ones; and (3) a nuanced inverted U-shaped adoption curve that identifies the urban middle class as the system's primary user base.

Problem

• Mapping urban socio-economic divides using bike-sharing mobility data
• Overcoming fine-grained socio-economic data scarcity with LLM enrichment
• Identifying housing price as dominant predictor of bike-sharing patterns

Innovation

• Used LLM to enrich bike trip data with economic attributes
• Applied Random Forest model to interpret mobility patterns
• Identified housing price as key predictor of bike-sharing usage